Search results
Results from the WOW.Com Content Network
JAMA is a software library for performing numerical linear algebra tasks created at National Institute of Standards and Technology in 1998 similar in functionality to LAPACK. Functionality [ edit ]
Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping. [3] [4] [5] As of November 2018, the tidyverse package and some of its individual packages comprise 5 out of the top 10 most downloaded R packages. [6] The tidyverse is the subject of multiple books and papers.
Free 3-clause BSD: Numerical linear algebra library with long history librsb: Michele Martone C, Fortran, M4 2011 1.2.0 / 09.2016 Free GPL: High-performance multi-threaded primitives for large sparse matrices. Support operations for iterative solvers: multiplication, triangular solve, scaling, matrix I/O, matrix rendering.
GAUSS is a matrix programming language for mathematics and statistics, developed and marketed by Aptech Systems.Its primary purpose is the solution of numerical problems in statistics, econometrics, time-series, optimization and 2D- and 3D-visualization.
The Comprehensive R Archive Network (CRAN) is R's central software repository, supported by the R Foundation. [9] It contains an archive of the latest and previous versions of the R distribution, documentation, and contributed R packages. [10] It includes both source packages and pre-compiled binaries for Windows and macOS. [11]
In Python NumPy arrays implement the flatten method, [note 1] while in R the desired effect can be achieved via the c() or as.vector() functions. In R, function vec() of package 'ks' allows vectorization and function vech() implemented in both packages 'ks' and 'sn' allows half-vectorization. [2] [3] [4]
In version 3.7.2, a package manager was added to allow the easier installation of extension packages. [6] Some functionality that used to be included with Weka prior to this version has since been moved into such extension packages, but this change also makes it easier for others to contribute extensions to Weka and to maintain the software, as this modular architecture allows independent ...
In statistical learning point of view, the matrix completion problem is an application of matrix regularization which is a generalization of vector regularization. For example, in the low-rank matrix completion problem one may apply the regularization penalty taking the form of a nuclear norm () = ‖ ‖